google-ai-edge/mediapipe-samples
Jan 2026 – Jan 2026
1 Month active
Languages Used
Python
Technical Skills
Computer VisionData AnalysisData VisualizationImage ProcessingJupyter NotebookMachine Learning
Harish P
PROFILE
Harish P
Harish contributed to the google-ai-edge/mediapipe-samples repository by enhancing segmentation demos and pose landmark visualizations. He improved notebook usability by clearing outputs, making demonstrations more readable and maintainable. Addressing a key bug, Harish refactored segmentation mask handling by squeezing category masks to 2D, which resolved broadcasting issues and increased the accuracy and robustness of both image and interactive segmentation. He also updated pose landmark visualizations to use the tasks API, resulting in better performance and clearer visuals. His work leveraged Python, Jupyter Notebook, and MediaPipe, demonstrating a solid understanding of computer vision, image processing, and data visualization workflows.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
301
Activity Months1
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Work History
January 2026
4 Commits • 2 Features
Jan 1, 2026January 2026 performance summary for google-ai-edge/mediapipe-samples. Key features delivered: Notebook cleanup for segmentation demos (clearing outputs to improve readability and reduce clutter) and Pose landmarks visualization update using the tasks API for better performance and clearer visuals. Major bugs fixed: Segmentation mask broadcasting fixes by squeezing category masks to 2D, ensuring correct dimensions and improving accuracy and robustness in image and interactive segmentation. Overall impact and accomplishments: Enhanced reliability and clarity of segmentation demos, improved visualization quality, and better maintainability by aligning rendering with the tasks API. Technologies/skills demonstrated: Python, Jupyter notebooks, MediaPipe segmentation, 2D mask handling, tasks API integration, visualization techniques. Business value: more dependable demos for stakeholders, reduced debugging time in segmentation pipelines, and faster onboarding for contributors due to clearer, API-driven visualization workflows.
4 Commits • 2 Features
Jan 1, 2026January 2026 performance summary for google-ai-edge/mediapipe-samples. Key features delivered: Notebook cleanup for segmentation demos (clearing outputs to improve readability and reduce clutter) and Pose landmarks visualization update using the tasks API for better performance and clearer visuals. Major bugs fixed: Segmentation mask broadcasting fixes by squeezing category masks to 2D, ensuring correct dimensions and improving accuracy and robustness in image and interactive segmentation. Overall impact and accomplishments: Enhanced reliability and clarity of segmentation demos, improved visualization quality, and better maintainability by aligning rendering with the tasks API. Technologies/skills demonstrated: Python, Jupyter notebooks, MediaPipe segmentation, 2D mask handling, tasks API integration, visualization techniques. Business value: more dependable demos for stakeholders, reduced debugging time in segmentation pipelines, and faster onboarding for contributors due to clearer, API-driven visualization workflows.
January 2026
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Computer VisionData AnalysisData VisualizationImage ProcessingJupyter NotebookMachine Learningdata analysisimage processingmachine learning
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